1.7k citations · 3.7k across the 65 of their papers we have counts for
68 papers
Symmetry-based modal analysis of heat transport in molecular dynamics of quasi-1D systems
Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen +1
Detailed analysis of thermal conductivity results obtained from molecular dynamics (MD) trajectories conventionally relies on knowledge of the harmonic vibrational modes of the sys…
Pulgon-tools: A toolkit for analysing and harnessing symmetries in quasi-1D systems
Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen +1
Pulgon-tools is an open-source software package providing building blocks for the analysis and modeling of quasi-one-dimensional (quasi-1D) periodic systems based on line-group the…
Structural Optimization in Tensor LEED Using a Parameter Tree and -Factor Gradients
Alexander M. Imre, Paul Haidegger, Florian Kraushofer +10
Quantitative low-energy electron diffraction [LEED ] is a powerful method for surface-structure determination, based on a direct comparison of experimentally observed …
msmJAX: Fast and Differentiable Electrostatics on the GPU in Python
Florian Buchner, Johannes Schörghuber, Nico Unglert +2
We present msmJAX, a Python package implementing the multilevel summation method with B-spline interpolation, a linear-scaling algorithm for efficiently evaluating electrostatic an…
Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems
Sandro Wieser, YuJie Cen, Georg K. H. Madsen +1
Atomistic simulations of heat transport in complex materials are costly and hard to converge. This has led to the development of several noise-reduction techniques applicable to eq…
Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective
Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen +1
Due to their aspect ratio and wide range of thermal conductivities, nanotubes hold significant promise as heat-management nanocomponents. Their practical use is, however, often lim…
Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning
Manuel Gallego, Sebastián Roca-Jerat, David Zueco +1
Machine-learning (ML) ansätze have greatly expanded the accuracy and reach of variational quantum Monte Carlo (QMC) calculations, in particular when exploring the manifold quantum…
Computing Anharmonic Infrared Spectra of Polycyclic Aromatic Hydrocarbons Using Machine-Learning Molecular Dynamics
Xinghong Mai, Zhao Wang, Lijun Pan +4
We introduce a machine learning molecular dynamics (MLMD) approach to calculate the anharmonic infrared (IR) absorption spectra of polycyclic aromatic hydrocarbons (PAHs), key carr…
Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study
Elin Dypvik Sødahl, Jesús Carrete, Georg K. H. Madsen +1
Hybrid molecular ferroelectrics with orientationally disordered mesophases offer significant promise as lead-free alternatives to traditional inorganic ferroelectrics owing to prop…
Machine-learning potential for phonon transport in AlN with defects in multiple charge states
Ying Dou, Koji Shimizu, Jesús Carrete +2
Understanding phonon transport properties in defect-laden AlN is important for their device applications. Here, we construct a machine-learning potential to describe phonon transpo…
First- and second-order quantum phase transitions in the long-range unfrustrated antiferromagnetic Ising chain
Víctor Herráiz-López, Sebastián Roca-Jerat, Manuel Gallego +4
We study the ground-state phase diagram of an unfrustrated antiferromagnetic Ising chain with longitudinal and transverse fields in the full range of interactions: from all-to-all…
Neural-network-enabled molecular dynamics study of HfO phase transitions
Sebastian Bichelmaier, Jesús Carrete, Georg K. H. Madsen
The advances of machine-learned force fields have opened up molecular dynamics (MD) simulations for compounds for which ab-initio MD is too resource-intensive and phenomena for whi…
Transformer Wave Function for Quantum Long-Range models
Sebastián Roca-Jerat, Manuel Gallego, Fernando Luis +2
We employ a neural-network architecture based on the Vision Transformer (ViT) architecture to find the ground states of quantum long-range models, specifically the transverse-field…
A neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia
Sebastian Bichelmaier, Jesús Carrete, Ralf Wanzenböck +2
Phonon-based approaches and molecular dynamics are widely established methods for gaining access to a temperature-dependent description of material properties. However, when a comp…
Quantitative predictions of the thermal conductivity in transition metal dichalcogenides: The impact of point defects in MoS and WS monolayers
Srinivisan Mahendran, Jesús Carrete, Andreas Isacsson +2
Transition metal dichalcogenides are investigated for various applications at the nanoscale thanks to their unique combination of properties and dimensionality. For many of the ant…
Neural-Network Force Field Backed Nested Sampling: Study of the Silicon p-T Phase Diagram
N. Unglert, J. Carrete, L. B. Pártay +1
Nested sampling is a promising method for calculating phase diagrams of materials, however, the computational cost limits its applicability if ab-initio accuracy is required. In th…
Electron-induced non-monotonic pressure dependence of the lattice thermal conductivity of θ-TaN
Ashis Kundu, Yani Chen, Xiaolong Yang +5
Recent theoretical and experimental research suggests that -TaN is a semimetal with high thermal conductivity (), primarily due to the contribution of phonons ($κ_\texttt{ph}…
Deep Ensembles vs. Committees for Uncertainty Estimation in Neural-Network Force Fields: Comparison and Application to Active Learning
Jesús Carrete, Hadrián Montes-Campos, Ralf Wanzenböck +2
A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation wi…
Hydrodynamic signatures in thermal transport in devices based on 2D materials: an ab initio study
Martí Raya-Moreno, Jesús Carrete, Xavier Cartoixà
We investigate the features arising from hydrodynamic effects in graphene and phosphorene devices with finite heat sources, using ab initio calculations to go beyond Callaway's mod…
BTE-Barna: An extension of almaBTE for thermal simulation of devices based on 2D materials
Martí Raya-Moreno, Xavier Cartoixà, Jesús Carrete
We present BTE-Barna (Boltzmann Transport Equation - Beyond the Rta for NAnosystems), a software package that extends the Monte Carlo (MC) module of the almaBTE solver of the Peier…
Accurate effective harmonic potential treatment of the high-temperature cubic phase of Hafnia
Sebastian Bichelmaier, Jesús Carrete, Michael Nelhiebel +1
HfO is an important high- dielectric and ferroelectric, exhibiting a complex potential energy landscape with several phases close in energy. It is, however, a strongly anhar…
A Differentiable Neural-Network Force Field for Ionic Liquids
Hadrián Montes-Campos, Jesús Carrete, Sebastian Bichelmaier +2
We present NeuralIL, a model for the potential energy of an ionic liquid that accurately reproduces first-principles results with orders-of-magnitude savings in computational cost.…
How do defects limit the ultrahigh thermal conductivity of BAs? A first principles study
Mauro Fava, Nakib Haider Protik, Chunhua Li +6
The promise enabled by BAs high thermal conductivity in power electronics cannot be assessed without taking into account the reduction incurred when doping the material. Using firs…
Machine-learning prediction of infrared spectra of interstellar polycyclic aromatic hydrocarbons
Peter Kovacs, Xiaosi Zhu, Jesus Carrete +2
We design and train a neural network (NN) model to efficiently predict the infrared spectra of interstellar polycyclic aromatic hydrocarbons (PAHs) with a computational cost many o…